Natural genetic variation in wild-derived mice controls host survival and transcriptional responses during endotoxic shock

Innate immune cells sense microbial danger signals such as bacterial lipopolysaccharide (LPS) through pattern recognition receptors, resulting in dynamic transcriptional reprogramming and rapid inflammatory responses. If not properly regulated, such responses can be detrimental to the host, as is seen in septic shock. A better understanding of the genetic regulation of transcriptional responses during endotoxemia could provide potential therapeutic insights. However, the majority of animal model studies have been performed using classic inbred laboratory strains of mice, typically C57BL/6 (B6), capturing limited genetic diversity and representing a potential blind spot. Here, we compared classic inbred B6 mice with wild-derived and genetically divergent PWD/PhJ (PWD) mice using in vivo and in vitro models of endotoxemia, to determine the role of natural genetic variation in septic shock. Compared with B6 mice, PWD mice were markedly resistant to LPS-induced endotoxic shock. Using LPS stimulation of bone marrow derived dendritic cells (BMDC) and RNA sequencing, we demonstrate that B6 and PWD BMDCs exhibit partially overlapping, yet highly divergent transcriptional responses, with B6 skewed towards stereotypical proinflammatory pathway activation, and PWD engaging regulatory or developmental pathways. To dissect genetic regulation of inflammatory responses by allelic variants, we used BMDCs from a sub-consomic strain carrying a ~50 Mb PWD-derived portion of chromosome 11 on a B6 background. This identified a subset of cis regulated and a large number of trans regulated genes. Bioinformatic analyses identified candidate trans regulators encoded in the chromosome 11 locus as transcription factors Irf1, Ncor1, and Srebf1. Taken together, our results demonstrate that natural genetic variation exerts a pronounced effect on host survival and transcriptional reprogramming during endotoxemia, suggesting possibilities for prediction of sepsis risk and/or personalized therapeutic interventions.